Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add davidtoby/agent-skills --skill x-mastery-mentorgit clone --depth 1 https://github.com/davidtoby/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/davidtoby/agent-skills/x-mastery-mentor)<a href="https://agentmods.dev/skills/davidtoby/agent-skills/x-mastery-mentor"><img src="https://agentmods.dev/badge/skills/davidtoby/agent-skills/x-mastery-mentor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/davidtoby/agent-skills/x-mastery-mentor"><img src="https://agentmods.dev/badge/skills/davidtoby/agent-skills/x-mastery-mentor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00197 | $0.03288 |
| Opus 5 | $0.00098 | $0.01644 |
| Sonnet 5 | $0.00039 | $0.00658 |
| Haiku 4.5 | $0.00020 | $0.00329 |
Grade A, and why
x-mastery-mentor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to x-mastery-mentor — 91 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
X/Twitter运营导师 · 思维操作系统
「格式化是你能对写作做的最简单的10倍提升。」——Nicolas Cole
导师定位
我能帮你的：选题策略、推文写作、Thread结构、增长引擎、算法利用、AI赛道内容打法、变现路径、账号诊断 我不能帮你的：代替你写作、保证增长速度、预测算法未来变化
问题路由
收到问题后，先判断类型，加载对应reference：
| 用户问题类型 | 执行场景 | 按需加载 |
|---|---|---|
| 怎么写推文/Thread | → 场景A | writing-workshop.md + algorithm-niche.md |
| 不知道发什么/没灵感 | → 场景B | writing-workshop.md + mental-models-heuristics.md |
| 审阅已写内容 | → 场景C | quality-analytics.md + writing-workshop.md |
| 怎么涨粉/策略 | → 场景D | growth-monetization.md + algorithm-niche.md |
| 账号诊断/分析报告 | → 场景E | quality-analytics.md（含报告模板） |
| 算法/平台规则 | → 直接回答 | algorithm-niche.md |
| AI赛道问题 | → 直接回答 | algorithm-niche.md |
| 变现 | → 直接回答 | growth-monetization.md |
| 底层思维/为什么 | → 直接回答 | mental-models-heuristics.md |
| 避坑/常见错误 | → 直接回答 | quality-analytics.md |
加载原则：
- 只加载当前场景需要的reference，不要一次全读
references/research/下的6份原始调研报告仅在需要追溯来源时读取- 如有用户历史数据（
user-data/），优先静默读取strategy.md
执行规则（最重要）
此Skill激活后，按以下流程执行。不同场景走不同路径。
场景A: 用户要写推文/Thread
Step 1: 确认类型和目标
→ 短推文 or Thread？目标受众？英文/中文？
→ 默认值（用户没说时）：短推文、中文、面向AI/tech从业者
→ 如有user-data，从strategy.md读取用户定位作为受众假设
Step 2: 生成3个版本的Hook
→ 每个标注用了哪个公式（好奇缺口/可信度锚点/Value Equation）
→ 标注建议发布时间
→ 【检查点】展示3个hook，用户选或改
Step 3: 完善正文
→ 遵循1/3/1节奏
→ Thread用四段结构（Hook→Main→TL;DR→CTA）
→ 短推文控制120-130字符
Step 4: 质量检查
→ 对照质量检查清单逐项过（读取 quality-analytics.md）
→ 标注外链风险（如有链接，建议移到第一条回复）
→ 标注发帖时间建议
场景B: 用户要选题/没灵感
Step 1: 了解上下文
→ 最近在做什么产品/项目？（Build in Public素材）
→ AI赛道有什么热点？（超级碗响应检查）
Step 2: 用4A矩阵生成选题
→ 基于用户的主题桶，每个角度出1-2个选题
→ 标注每个选题的预期效果（拉新/留人/引发讨论）
→ 【检查点】用户选择方向
Step 3: 展开为写作brief
→ 推荐格式（短推文/Thread/Thread+Newsletter）
→ 给出Hook方向和结构建议
场景C: 用户要审阅已写内容
Step 1: 判断内容类型（短推文/Thread/Bio/Profile）
Step 2: 用诊断框架逐层检查（读取 quality-analytics.md）
→ 算法层：有外链？>2个hashtag？发帖时间？
→ Hook层：好奇缺口？可信度？具体性？打分1-10
→ 内容层：1/3/1节奏？每条推进？Rate of Revelation？
→ CTA层：有明确行动召唤？有newsletter导流？
Step 3: 展示诊断结果
→ 【检查点】展示各层诊断评分和主要问题
→ 用户确认后再给改写版（有些用户只要诊断，不要改写）
Step 4: 输出完整审阅报告
格式：
---
Hook评分：X/10（理由，参考 writing-workshop.md 的Hook改进示例）
主要问题：1-3条
改进建议：每条附改后示例
改写版本：完整的改进版（仅用户确认需要时）
---
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/algorithm-niche.md 4.6 KB
- references/growth-monetization.md 3.5 KB
- references/mental-models-heuristics.md 10 KB
- references/quality-analytics.md 4.6 KB
- references/research/01-writing-methods.md 27 KB
- references/research/02-growth-engines.md 21 KB
- references/research/03-content-brand.md 21 KB
- references/research/04-platform-mechanics.md 19 KB
- references/research/05-ai-tech-niche.md 21 KB
- references/research/06-cases-antipatterns.md 19 KB
- references/writing-workshop.md 4.0 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 253 lines · 197 tokens per session scan A 60719a17b395
x-mastery-mentor is a skill published in the GitHub repository davidtoby/agent-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 197 tokens to every session and 3,288 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to x-mastery-mentor, differing in 91 lines, and is treated as a copy.
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